dyneth02/FDM-Labs
Comprehensive machine learning framework for genomic analysis and predictive modeling. This repository showcases advanced classification and clustering techniques using XGBoost, CatBoost, LightGBM, and RandomForest to identify genetic disorders. Includes association rule mining with Apriori and unsupervised geographical clustering via KMeans.
This framework helps geneticists and researchers quickly analyze complex genomic data to identify genetic disorders and their specific subtypes. It takes in raw or pre-processed genomic datasets and outputs predictions of genetic disorders, along with insights into patterns and relationships within the genetic information. The ideal users are biomedical researchers, genetic counselors, and clinical scientists working with large-scale genetic screening or diagnostic data.
Use this if you need to classify genetic disorders from genomic data, discover hidden patterns in gene associations, or cluster genomic information to identify distinct groups.
Not ideal if you are looking for a tool to perform basic statistical analysis or visualize simple genetic relationships without advanced machine learning techniques.
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Language
Jupyter Notebook
License
MIT
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Last pushed
Dec 25, 2025
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